Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.

Predicting the effect of mutations on protein stability using a simple orientational potential.

Some notes (cookbook) for pyMol. Protein Crystallography course.

Prediction of protein thermodynamic stability changes upon mutations through a Gaussian Network Model simulating protein unfolding behavior

Predicting the effect of mutations on protein stability and protein-protein interaction affinity.

A workflow to get rid of redundant mutations

https://biohackathon.biolib.com/event/2021-protein-edition/ - team "house-of-mutants" - task "Predicting multi-mutant protein stability"

Snakemake pipeline for Rosetta 'cartesian-ddg' protocol for protein stability prediction upon mutations.

Implementation of Abyssal, a deep neural network trained with a new "mega" dataset to predict the impact of an amino acid variant on protein stability.

Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch.

Two R shiny apps developed for analyzing differential scanning fluorimetry (DSF) data. One for binding, and one for stability

R shiny app to analyse circular dichroism data

Identify the thermostable mutations in enzymes

Official repository for the paper "Few-shot Prediction of the experimental functional measurements for proteins with single point mutations".

Large data set of thermal stabilities for mutants of BglB, and associated publication

calculating ddg of point-mutation using contact potentials